1. Technical Field
The invention relates generally to the field of customer service quality assurance management. More specifically, the invention relates to data-mining customer-agent interactions and survey data for ensuring quality customer service experience throughout a customer service lifecycle.
2. Description of the Related Art
Customer service management traditionally includes conducting a service call between a customer and a customer service agent and recording information about the interaction after it ends. The step of recording performance of a customer service representative is most often carried out by the customer service agent personally through self-reporting. Other times, a customer is asked to fill out a survey relating to the call subsequent to the interaction. In the vast majority of cases, the survey asks the customer to comment on a variety of topics, i.e. operational metrics. However, this approach focuses only on analyzing previously-identified operational metrics and typically does not involve all of the information that is important to an individual customer. Indeed, these traditional mechanisms return little information and oftentimes no useful information.
The traditional mechanisms are also limited to the type of information that they are able to gather about the level of customer service offered by an agent during an interaction. First, in the case of agent self-reporting, an agent has an inherent bias to stress the positive aspects of the service call and downplay the negative ones. Furthermore, the agent may not even realize what went right during a call or what went wrong because the customer may hide their true disposition. Likewise, customer surveys are limited to quantifiable answers indicated with checkboxes and do not convey enough information for robust analysis. Also, current strategies of customer service data gathering do not provide for specific customer responses to specific questions designed by a particular manufacturer or vender of goods and services. Likewise, they do not allow for unconstrained customer feedback in a useful manner.
Furthermore, current solutions do not delve into the reasoning behind the answers to surveys. Additionally, surveys are susceptible to sample bias since the responses are offered by a self-selected segment of customers, i.e. those who are either very satisfied or very dissatisfied. Also, sample sizes are currently too small for a robust analysis. Additionally, current solutions cannot evolve based on changing customer needs and perception.